A fundamental problem in computer vision and image processing is image segmentation, or object extraction, in intensity, infrared, or other images. Examples of the applications to which this problem applies is object recognition, automatic target recognition, and scene analysis, not to mention monitoring and tracking of objects. Hence it is of great importance to have a dependable automated technique for target extraction. Current techniques require either user-supplied parameters or model-based templates to extract the pixels corresponding to a desired image from an overall image. Examples of techniques requiring user-supplied parameters include thresholding and edge detection. They are convenient, but they both require threshold values which, in general, cannot be calculated automatically. Model-based techniques may not require the supervision of a human operator, but they have other deficiencies: they are slow since the image orientation or scale may be different from that of the template, and they are crude and not very dependable since the object pose in the image and the model pose in the template may not agree. Thus model-based techniques have a low degree of resolution; for example, they may not distinguish between two types of aircraft since aircraft would look similar when compared to aircraft templates.
Of particular interest is infrared images, if only because all engines and living creatures give off heat, and thus appear bright in infrared images. It is well known that the detection of an object (or target) in an infrared image depends on its thermal contrast with the background. Accordingly, measures of target detectability (target metric), such as the difference between the mean target and background temperatures, or its modified versions also incorporating the standard deviation of the target's thermal variations, have been defined. These measures have been used to estimate the probability of the target detection by a human observer. They have thus been helpful in the design of infrared imaging systems. Typically, autonomous tracking of an infrared target begins after the detection and the identification of the target by a human operator. By knowing the type and the size (in the image) of the target, an appropriate template is supplied to the tracking system. The autonomous tracking then continues by modeling the target with the template and finding its best match in a sequence of images.
The above techniques are not (fully) automated. For automatic object extraction, image thresholding has received considerable attention. The problem however is how to calculate the threshold value. For this, a variety of techniques based on the image gray-level histogram have been proposed. The threshold is taken to be the gray level at the valley of a (hopefully) bimodal histogram-one having a val separating two histogram peaks, with the peaks representing the object and the background, respectively. A frequent problem is that the background itself may be multimodal (i.e. contain plural objects). Furthermore, the valley is often gradual, and its bottom is not well defined, often making the calculation of the threshold less accurate than needed.